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Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. Significance: These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development. See related commentary by Kuijjer, p. 3072

We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual information to perform variable selection; during the second stage, we use scalable Bayesian network (BN) modeling to identify candidate genes and their interactions. Two crucial features of the proposed approach are (i) the ability to handle mixed data types (continuous and discrete, genomic, epigenomic, etc.), and (ii) a flexible boundary between the variable selection and network modeling stages --- the boundary that can be adjusted in accordance with the investigators’ BN software scalability and hardware implementation. These two aspects result in high generalizability of the proposed analytical framework. We apply the above strategy to three different TCGA datasets (LGG, Brain Lower Grade Glioma; HNSC, Head and Neck Squamous Cell Carcinoma; STES, Stomach and Esophageal Carcinoma), linking multimodal molecular information (SNPs, mRNA expression, DNA methylation) to two clinical outcome variables (tumor status and patient survival). We identify 11 candidate genes, of which 6 have already been directly implicated in the cancer literature. One novel LGG prognostic factor suggested by our analysis, methylation of TMPRSS11F type II transmembrane serine protease, presents intriguing direction for the follow-up studies.

Recent developments in sequencing and growth of bioinformatics resources provide us with vast depositories of protein network and single nucleotide polymorphism data. It allows us to re-examine, on a larger and more comprehensive scale, the relationship between protein–protein interactions and protein variability and evolutionary rates. This relationship has remained far from unambiguously resolved for quite a long time, reflecting shifting analysis approaches in the literature, and growing data availability. In this study, we utilized several public genomic databases to investigate this relationship in human, mouse, pig, chicken, and zebrafish. We observed strong non-linear relationship patterns (tending towards convex decreasing function shapes) between protein variability and the density of corresponding protein–protein interactions across all five species. To investigate further, we carried out stochastic simulations, modeling the interplay between protein connectivity and variability. Our results indicate that a simple negative linear correlation model, often suggested (or tacitly assumed) in the literature, as either a null or an alternative hypothesis, is not a good fit with the observed data. After considering different (but still relatively simple, and not overfitting) simulation models, we found that a convex decreasing protein variability–connectivity function (specifically, exponential decay) led to a much better fit with the real data. We conclude that simple correlation models might be inadequate for describing protein variability–connectivity interplay in vertebrates; they often tend towards false negatives (showing no more than marginal linear or rank correlation where there are in fact strong non-random patterns). Electronic supplementary material The online version of this article (10.1007/s00239-019-09899-z) contains supplementary material, which is available to authorized users.

Diffuse gliomas are malignant neoplasms originating in the parenchyma of the central nervous system whose cellular origin remains elusive. To determine the cellular and spatiotemporal origin of gliomas, we devised a three-dimensional reconstruction of tumor lineage. We used neuronavigation to acquire eight to twelve image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma, which we characterized using DNA methylation arrays. A total of 133 samples were obtained from regions with and without imaging abnormalities. Methylation profiles were analyzed to construct phyloepigenetic trees and subsequently projected on 3D image-derived tumor maps. Lineage analysis of these evolutionary trees indicated that the sampled gliomas largely evolved stochastically, suggesting that critical tumor drivers were acquired early in time. These results were further validated using 102 multi-region samples from 24 independent patients. Patristic (evolutionary) and cartesian (spatial) distances between pairs of tumor samples from the same patient demonstrated strong correlations, suggesting that this information could be used to determine trajectories of tumor evolution. Evolutionary and spatial distance metrics were combined with histologically obtained and computationally quantified cellularity and proliferation rates to model the direction and magnitude of tumor growth. In order to relate tumor lineage to brain anatomy we mapped patient imaging to a reference space (Montreal Neurological Institute, MNI). Samples mapping to regions of white matter were earlier in tumor lineage when compared to samples mapping to regions of gray matter, suggesting that white matter involvement is an early feature of tumor development. Samples early in tumor lineage were located closer to the ventricles when compared with samples late in lineage, suggesting that tumors grow outward from the ventricular lining. Finally, we used a tumor probability map constructed using imaging from over 500 unrelated patients to associate lineage to tumor probability. Results from this analysis indicated that periventricular areas of high tumor probability coincided with samples early in tumor lineage and cortical areas of low tumor probability coincided with samples late in tumor lineage. Taken together, our phylogeographic analysis of tumor development supports a neural progenitor cell-of-origin model, where neural stem cells in the subventricular zone of the lateral ventricles give rise to mature tumors. Citation Format: Floris P. Barthel, Niels Verburg, Russell Rockne, Roelant Eijgelaar, Kevin Anderson, Domenique Müller, Sergio Branciamore, Kevin C. Johnson, Pieter Wesseling, Philip C. de Witt Hamer, Roel G. Verhaak. Stereotactic image-guided epigenome profiling reveals a neural stem cell evolutionary origin of diffuse gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3147.

Summary The challenges in recapitulating in vivo human T cell development in laboratory models have posed a barrier to understanding human thymopoiesis. Here, we used single-cell RNA sequencing (sRNA-seq) to interrogate the rare CD34+ progenitor and the more differentiated CD34– fractions in the human postnatal thymus. CD34+ thymic progenitors were comprised of a spectrum of specification and commitment states characterized by multilineage priming followed by gradual T cell commitment. The earliest progenitors in the differentiation trajectory were CD7– and expressed a stem-cell-like transcriptional profile, but had also initiated T cell priming. Clustering analysis identified a CD34+ subpopulation primed for the plasmacytoid dendritic lineage, suggesting an intrathymic dendritic specification pathway. CD2 expression defined T cell commitment stages where loss of B cell potential preceded that of myeloid potential. These datasets delineate gene expression profiles spanning key differentiation events in human thymopoiesis and provide a resource for the further study of human T cell development.

Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensional temporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identified step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development.Graphical AbstractIn briefThe theory of state-transition is applied to acute myeloid leukemia (AML) to model transcriptome dynamics and trajectories in a state-space, and is used to identify critical points corresponding to critical transcriptomic perturbations that predict leukemia development.HighlightsLeukemia transcriptome dynamics are modeled as movement in transcriptome state-spaceState-transition model and critical points accurately predicts leukemia developmentCritical point-based approach identifies step-wise transcriptome events in leukemiaState-based geometric analysis provides quantification of leukemogenic contribution

Significance We report here that a recently developed Bayesian network (BN) methodology and software platform yield useful information when applied to the analysis of intrachromosomal interaction datasets combined with Encyclopedia of DNA Elements publicly available datasets for the B-lymphocyte cell line GM12878. Of 106 variables analyzed, interaction strength between DNA segments was found to be directly dependent on only four types of variables: distance, Rad21 or SMC3 (cohesin components), transcription at transcription start sites, and the number of CCCTC-binding factor (CTCF)–cohesin complexes between interacting DNA segments. The importance of directionally oriented ctcf motifs was confirmed not only for loops but also for enhancer–promoter interactions. Purely data-driven BN analyses also identified known critical, lineage-determining transcription factors (TFs) as well as some potentially new dependencies between TFs., Long-range intrachromosomal interactions play an important role in 3D chromosome structure and function, but our understanding of how various factors contribute to the strength of these interactions remains poor. In this study we used a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly available datasets for intrachromosomal interactions. We investigated how 106 variables affect the pairwise interactions of over 10 million 5-kb DNA segments in the B-lymphocyte cell line GB12878. Strictly data-driven BN modeling indicates that the strength of intrachromosomal interactions (hic_strength) is directly influenced by only four types of factors: distance between segments, Rad21 or SMC3 (cohesin components),transcription at transcription start sites (TSS), and the number of CCCTC-binding factor (CTCF)–cohesin complexes between the interacting DNA segments. Subsequent studies confirmed that most high-intensity interactions have a CTCF–cohesin complex in at least one of the interacting segments. However, 46% have CTCF on only one side, and 32% are without CTCF. As expected, high-intensity interactions are strongly dependent on the orientation of the ctcf motif, and, moreover, we find that the interaction between enhancers and promoters is similarly dependent on ctcf motif orientation. Dependency relationships between transcription factors were also revealed, including known lineage-determining B-cell transcription factors (e.g., Ebf1) as well as potential novel relationships. Thus, BN analysis of large intrachromosomal interaction datasets is a useful tool for gaining insight into DNA–DNA, protein–DNA, and protein–protein interactions.

The identity/recognition of tRNAs, in the context of aminoacyl tRNA synthetases (and other molecules), is a complex phenomenon that has major implications ranging from the origins and evolution of translation machinery and genetic code to the evolution and speciation of tRNAs themselves to human mitochondrial diseases to artificial genetic code engineering. Deciphering it via laboratory experiments, however, is difficult and necessarily time- and resource-consuming. In this study, we propose a mathematically rigorous two-pronged in silico approach to identifying and classifying tRNA positions important for tRNA identity/recognition, rooted in machine learning and information-theoretic methodology. We apply Bayesian Network modeling to elucidate the structure of intra-tRNA-molecule relationships, and distribution divergence analysis to identify meaningful inter-molecule differences between various tRNA subclasses. We illustrate the complementary application of these two approaches using tRNA examples across the three domains of life, and identify and discuss important (informative) positions therein. In summary, we deliver to the tRNA research community a novel, comprehensive methodology for identifying the specific elements of interest in various tRNA molecules, which can be followed up by the corresponding experimental work and/or high-resolution position-specific statistical analyses.

The discovery of ribozymes, RNAs with catalytic activity, revealed the extraordinary characteristic of this molecule, and corroborated the idea that RNA was the first informative polymer. The "RNA world" hypothesis asserts that the DNA/RNA/PROTEIN world arose from an earlier RNA world in which were present only RNA molecules able to perform both of the two functions performed separately by DNA and proteins in the present-day cells: the ability to transfer genetic information and to carry out catalytic activity. The catalytic properties of ribozymes are exclusively due to the capacity of RNA molecules to assume particular structures. Moreover, the structural versatility of RNA can allow to a single RNA sequence to fold in more than one structure, able to perform more than one function. In the first part of this work we will discuss the RNA plasticity, focusing on "bifunctional" ribozymes isolated by in vitro selection experiments, and on the consequences of this plasticity in the prospective of the emergence of new specific functions. The possibility that one sequence could have more than one structure/function, greatly increase the evolutionary potential of RNA, and the capacity of RNA to switch from a structure/function to another is probably one of the reasons of the evolutionary success also in modern-day cells. Naturally occurring ribozymes discovered in contemporary cells, demonstrate the crucial role that ribozymes still have in the modern protein world. In the second part of this paper we will discuss the capacity of natural ribozymes to modulate gene expression making use of their exclusive catalytic properties. Moreover, we will consider the possibility of their ancient origin.